From ICE Age to rEVolution
The Determinants of Green and Greenish Innovations in the Automotive Industry
Marco Guerzoni
(with N. Corrocher, A. Novaresio, T. Pierangeli)
UNIVPM Seminar – 13/02/2025
Ursula von der Leyen: “Europe must lead the green transition, and mobility is at the core of this transformation.”
Locomobile Steam Roundabout 1900
Jamais Contente 1899
| Variabile | Green | Greenish |
|---|---|---|
| EPS | -0.502*** | 0.045** |
| EPS^2 | 0.075*** | -0.068** |
| Log Green/ishPat | 0.097*** | 0.030*** |
| Log Green/ishPat^2 | 0.40** | ns |
| Relative Tech Advantage | 2.496*** | 0.040*** |
| Relative Tech Advantage^2 | -0.823** | 2.273*** |
| Green Vehicle % | 1.987*** | -0.656** |
| Material Footprint | -0.019 | -1.355** |
| GDP per Capita | 0.079*** | ns |
| GDP per Capita^2 | -0.001*** | ns |
$ echo marco.guerzoni@unimib.it $ echo Feel free to contact me for research collaborations or discussions. $ ping -c 1 127.0.0.1 PING 127.0.0.1 (127.0.0.1): 56 data bytes 64 bytes from 127.0.0.1: icmp_seq=0 ttl=64 time=0.045 ms $ echo "Bicocca is home."
After conditioning on Σt yit: \[ P(y_{i1},...,y_{iT}|\sum_{t=1}^T y_{it}) = \frac{\prod_{t=1}^T \exp(x_{it}\beta)^{y_{it}}}{\sum_{d_i \in D_i} \prod_{t=1}^T \exp(x_{it}\beta)^{d_{it}}} \]
For each unit i: \[ \hat{\alpha}_i = \ln\left(\frac{\sum_{t=1}^T y_{it}}{\sum_{t=1}^T \exp(x_{it}\hat{\beta} + \hat{\gamma}_t)}\right) \]
For each period t: \[ \hat{\gamma}_t = \ln\left(\frac{\sum_{i=1}^N y_{it}}{\sum_{i=1}^N \exp(x_{it}\hat{\beta} + \hat{\alpha}_i)}\right) \]
# Two-way FE Poisson
mod_twfe <- feglm(y ~ x | id + time,
family = "poisson",
data = panel_data)
# Retrieve FE
alphas <- fixef(mod_twfe)$id # Unit FE
gammas <- fixef(mod_twfe)$time # Time FE
# Compute SE
se_fe <- se(mod_twfe,
vcov = "cluster",
cluster = ~ id)